sync v0.15.1 (models)

vllm/model_executor/models/stablelm.py

@@ -246,7 +246,7 @@ class StableLMEpochModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -332,7 +332,7 @@ class StablelmForCausalLM(nn.Module, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/starcoder2.py

@@ -252,7 +252,7 @@ class Starcoder2Model(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None,
         inputs_embeds: torch.Tensor | None = None,
@@ -336,7 +336,7 @@ class Starcoder2ForCausalLM(nn.Module, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/step3_text.py

@@ -354,7 +354,7 @@ class Step3TextModel(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,
@@ -419,7 +419,7 @@ class Step3TextForCausalLM(nn.Module, SupportsPP):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/step3_vl.py

@@ -1101,7 +1101,7 @@ class Step3VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/ultravox.py

@@ -714,7 +714,7 @@ class UltravoxModel(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/voxtral.py

@@ -397,7 +397,7 @@ class VoxtralForConditionalGeneration(
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/voxtral_streaming.py

@@ -173,7 +173,7 @@ class VoxtralStreamingGeneration(VoxtralForConditionalGeneration):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         intermediate_tensors: IntermediateTensors | None = None,
         inputs_embeds: torch.Tensor | None = None,

vllm/model_executor/models/whisper_causal.py

@@ -105,7 +105,6 @@ def create_whisper_attention_backend_with_block_pooling(
 ) -> type[AttentionBackend]:
     prefix = "WhisperCausalAttentionWithBlockPooling_"
     underlying_builder = underlying_attn_backend.get_builder_cls()
-    underlying_impl = underlying_attn_backend.get_impl_cls()
 
     class WhisperCausalAttentionWithBlockPoolingBuilder(underlying_builder):  # type: ignore
         def __init__(
@@ -152,43 +151,6 @@ def create_whisper_attention_backend_with_block_pooling(
                 common_prefix_len, new_common_attn_metadata, fast_build
             )
 
-    # NOTE: We need a custom impl so we can use the transformed slot_mapping
-    # computed by `WhisperCausalAttentionWithBlockPoolingBuilder` instead of
-    # the one from `forward_context.slot_mapping` (gpu_model_runner).
-    # This follows the same pattern as CrossAttentionImpl.
-    class WhisperCausalAttentionWithBlockPoolingImpl(underlying_impl):  # type: ignore[valid-type,misc]
-        def forward(
-            self,
-            layer: torch.nn.Module,
-            query: torch.Tensor,
-            key: torch.Tensor,
-            value: torch.Tensor,
-            kv_cache: torch.Tensor,
-            attn_metadata: AttentionMetadata,
-            output: torch.Tensor | None = None,
-            output_scale: torch.Tensor | None = None,
-            output_block_scale: torch.Tensor | None = None,
-        ) -> torch.Tensor:
-            if (
-                not underlying_attn_backend.forward_includes_kv_cache_update
-                and attn_metadata is not None
-            ):
-                self.do_kv_cache_update(
-                    layer, key, value, kv_cache, attn_metadata.slot_mapping
-                )
-
-            return super().forward(
-                layer,
-                query,
-                key,
-                value,
-                kv_cache,
-                attn_metadata,
-                output,
-                output_scale,
-                output_block_scale,
-            )
-
     if not issubclass(underlying_attn_backend, FlashAttentionBackend):
         raise NotImplementedError(
             f"{underlying_attn_backend} is not yet supported."
@@ -201,7 +163,6 @@ def create_whisper_attention_backend_with_block_pooling(
         attention_backend_cls=underlying_attn_backend,
         overrides={
             "get_builder_cls": lambda: WhisperCausalAttentionWithBlockPoolingBuilder,
-            "get_impl_cls": lambda: WhisperCausalAttentionWithBlockPoolingImpl,
             "get_kv_cache_shape": lambda num_blocks,
             block_size,
             num_kv_heads,
@@ -214,7 +175,6 @@ def create_whisper_attention_backend_with_block_pooling(
                 num_kv_heads // block_pool_size,
                 head_size,
             ),  # TODO: generalize to other backends
-            "forward_includes_kv_cache_update": True,
         },
     )
 

vllm/model_executor/models/zamba2.py

@@ -771,7 +771,7 @@ class Zamba2Model(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         inputs_embeds: torch.Tensor | None = None,
     ) -> torch.Tensor | IntermediateTensors:
@@ -947,7 +947,7 @@ class Zamba2ForCausalLM(nn.Module, HasInnerState, IsHybrid, SupportsMambaPrefixC
 
     def forward(
         self,
-        input_ids: torch.Tensor | None,
+        input_ids: torch.Tensor,
         positions: torch.Tensor,
         inputs_embeds: torch.Tensor | None = None,
         **kwargs: Any,